Tele-Aloha: A Low-budget and High-authenticity Telepresence System Using Sparse RGB Cameras

• DOI: 10.48550/arxiv.2405.14866
• Publication Date: 2024-01-01
• Authors: Tu, Hanzhang; Shao, Ruizhi; Dong, Xue; Zheng, Shunyuan; Zhang, Hao; Chen, Lili; Wang, Meili; Li, Wenyu; Ma, Siyan; Zhang, Shengping; Zhou, Boyao; Liu, Yebin
• Keywords: FOS: Computer and information sciences; Computer Vision and Pattern Recognition (cs.CV); Computer Science - Computer Vision and Pattern Recognition

Paper accepted by SIGGRAPH 2024. Project page: http://118.178.32.38/c/Tele-Aloha/<br/>In this paper, we present a low-budget and high-authenticity bidirectional telepresence system, Tele-Aloha, targeting peer-to-peer communication scenarios. Compared to previous systems, Tele-Aloha utilizes only four sparse RGB cameras, one consumer-grade GPU, and one autostereoscopic screen to achieve high-resolution (2048x2048), real-time (30 fps), low-latency (less than 150ms) and robust distant communication. As the core of Tele-Aloha, we propose an efficient novel view synthesis algorithm for upper-body. Firstly, we design a cascaded disparity estimator for obtaining a robust geometry cue. Additionally a neural rasterizer via Gaussian Splatting is introduced to project latent features onto target view and to decode them into a reduced resolution. Further, given the high-quality captured data, we leverage weighted blending mechanism to refine the decoded image into the final resolution of 2K. Exploiting world-leading autostereoscopic display and low-latency iris tracking, users are able to experience a strong three-dimensional sense even without any wearable head-mounted display device. Altogether, our telepresence system demonstrates the sense of co-presence in real-life experiments, inspiring the next generation of communication.<br/>